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!720 fix doc problem 

Merge pull request !720 from JichenZhao/normfix
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mindspore-ci-bot 2020-04-27 19:57:55 +08:00 committed by Gitee
parent d69084f681 89b797ae86
commit 2538f0ba79
1 changed files with 8 additions and 2 deletions
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10
mindspore/nn/layer/normalization.py
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@ -17,6 +17,7 @@ from mindspore.ops import operations as P
from mindspore.ops import functional as F
from mindspore.common.parameter import Parameter
from mindspore.common.initializer import initializer
from mindspore.ops.primitive import constexpr
from mindspore.common.tensor import Tensor
import mindspore.common.dtype as mstype
import mindspore.context as context
@ -166,6 +167,10 @@ class _BatchNorm(Cell):
return 'num_features={}, eps={}, momentum={}, gamma={}, beta={}, moving_mean={}, moving_variance={}'.format(
self.num_features, self.eps, self.momentum, self.gamma, self.beta, self.moving_mean, self.moving_variance)
@constexpr
def _channel_check(channel, num_channel):
if channel != num_channel:
raise ValueError("the input channel is not equal with num_channel")
class BatchNorm1d(_BatchNorm):
r"""
@ -324,7 +329,7 @@ class GlobalBatchNorm(_BatchNorm):
Args:
num_features (int): `C` from an expected input of size (N, C, H, W).
device_num_each_group (int): The number of device in each group.
device_num_each_group (int): The number of devices in each group.
eps (float): A value added to the denominator for numerical stability. Default: 1e-5.
momentum (float): A floating hyperparameter of the momentum for the
running_mean and running_var computation. Default: 0.9.
@ -350,7 +355,7 @@ class GlobalBatchNorm(_BatchNorm):
Tensor, the normalized, scaled, offset tensor, of shape :math:`(N, C_{out}, H_{out}, W_{out})`.
Examples:
>>> global_bn_op = nn.GlobalBatchNorm(num_features=3, group=4)
>>> global_bn_op = nn.GlobalBatchNorm(num_features=3, device_num_each_group=4)
>>> input = Tensor(np.random.randint(0, 255, [1, 3, 224, 224]), mindspore.float32)
>>> global_bn_op(input)
"""
@ -507,6 +512,7 @@ class GroupNorm(Cell):
def construct(self, x):
batch, channel, height, width = self.shape(x)
_channel_check(channel, self.num_channels)
x = self.reshape(x, (batch, self.num_groups, channel*height*width/self.num_groups))
mean = self.reduce_mean(x, 2)
var = self.reduce_sum(self.square(x - mean), 2) / (channel * height * width / self.num_groups - 1)